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Wu M, Tao H, Xu T, Zheng X, Wen C, Wang G, Peng Y, Dai Y. Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases. Proteome Sci 2024; 22:7. [PMID: 39304896 DOI: 10.1186/s12953-024-00231-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 09/01/2024] [Indexed: 09/22/2024] Open
Abstract
Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research.
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Affiliation(s)
- Mengyao Wu
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Huihui Tao
- School of Medicine, Anhui University of Science & Technology, Huainan, China.
- Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institutes, Huainan, China.
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Huainan, China.
| | - Tiantian Xu
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Xuejia Zheng
- The First Hospital of Anhui University of Science and Technology, Huainan, China
| | - Chunmei Wen
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Guoying Wang
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Yali Peng
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Yong Dai
- School of Medicine, Anhui University of Science & Technology, Huainan, China
- The First Hospital of Anhui University of Science and Technology, Huainan, China
- Joint Research Center for Occupational Medicine and Health of IHM, Anhui University of Science and Technology, Huainan, China
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Zhao M, Che Y, Gao Y, Zhang X. Application of multi-omics in the study of traditional Chinese medicine. Front Pharmacol 2024; 15:1431862. [PMID: 39309011 PMCID: PMC11412821 DOI: 10.3389/fphar.2024.1431862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 08/28/2024] [Indexed: 09/25/2024] Open
Abstract
Traditional Chinese medicine (TCM) is playing an increasingly important role in disease treatment due to the advantages of multi-target, multi-pathway mechanisms, low adverse reactions and cost-effectiveness. However, the complexity of TCM system poses challenges for research. In recent years, there has been a surge in the application of multi-omics integrated research to explore the active components and treatment mechanisms of TCM from various perspectives, which aids in advancing TCM's integration into clinical practice and holds immense importance in promoting modernization. In this review, we discuss the application of proteomics, metabolomics, and mass spectrometry imaging in the study of composition, quality evaluation, target identification, and mechanism of action of TCM based on existing literature. We focus on the workflows and applications of multi-omics based on mass spectrometry in the research of TCM. Additionally, potential research ideas for future exploration in TCM are outlined. Overall, we emphasize the advantages and prospects of multi-omics based on mass spectrometry in the study of the substance basis and mechanism of action of TCM. This synthesis of methodologies holds promise for enhancing our understanding of TCM and driving its further integration into contemporary medical practices.
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Affiliation(s)
| | | | | | - Xiangyang Zhang
- School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China
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Luo G, Wang S, Lu W, Ju W, Li J, Tan X, Zhao H, Han W, Yang X. Application of metabolomics in oral squamous cell carcinoma. Oral Dis 2024; 30:3719-3731. [PMID: 38376209 DOI: 10.1111/odi.14895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/17/2024] [Accepted: 02/02/2024] [Indexed: 02/21/2024]
Abstract
BACKGROUND Oral squamous cell carcinoma (OSCC) is a prevalent malignancy affecting the head and neck region. The prognosis for OSCC patients remains unfavorable due to the absence of precise and efficient early diagnostic techniques. Metabolomics offers a promising approach for identifying distinct metabolites, thereby facilitating early detection and treatment of OSCC. OBJECTIVE This review aims to provide a comprehensive overview of recent advancements in metabolic marker identification for early OSCC diagnosis. Additionally, the clinical significance and potential applications of metabolic markers for the management of OSCC are discussed. RESULTS This review summarizes metabolic changes during the occurrence and development of oral squamous cell carcinoma and reviews prospects for the clinical application of characteristic, differential metabolites in saliva, serum, and OSCC tissue. In this review, the application of metabolomic technology in OSCC research was summarized, and future research directions were proposed. CONCLUSION Metabolomics, detection technology that is the closest to phenotype, can efficiently identify differential metabolites. Combined with statistical data analyses and artificial intelligence technology, it can rapidly screen characteristic biomarkers for early diagnosis, treatment, and prognosis evaluations.
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Affiliation(s)
- Guanfa Luo
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
- Department of Burn and Plastic Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Shuai Wang
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
| | - Wen Lu
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Wei Ju
- Department of Burn and Plastic Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Jianhong Li
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Xiao Tan
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Huiting Zhao
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Wei Han
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xihu Yang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, China
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Dannhorn A, Kazanc E, Flint L, Guo F, Carter A, Hall AR, Jones SA, Poulogiannis G, Barry ST, Sansom OJ, Bunch J, Takats Z, Goodwin RJA. Morphological and molecular preservation through universal preparation of fresh-frozen tissue samples for multimodal imaging workflows. Nat Protoc 2024; 19:2685-2711. [PMID: 38806741 DOI: 10.1038/s41596-024-00987-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 02/14/2024] [Indexed: 05/30/2024]
Abstract
The landscape of tissue-based imaging modalities is constantly and rapidly evolving. While formalin-fixed, paraffin-embedded material is still useful for histological imaging, the fixation process irreversibly changes the molecular composition of the sample. Therefore, many imaging approaches require fresh-frozen material to get meaningful results. This is particularly true for molecular imaging techniques such as mass spectrometry imaging, which are widely used to probe the spatial arrangement of the tissue metabolome. As high-quality fresh-frozen tissues are limited in their availability, any sample preparation workflow they are subjected to needs to ensure morphological and molecular preservation of the tissues and be compatible with as many of the established and emerging imaging techniques as possible to obtain the maximum possible insights from the tissues. Here we describe a universal sample preparation workflow, from the initial step of freezing the tissues to the cold embedding in a new hydroxypropyl methylcellulose/polyvinylpyrrolidone-enriched hydrogel and the generation of thin tissue sections for analysis. Moreover, we highlight the optimized storage conditions that limit molecular and morphological degradation of the sections. The protocol is compatible with human and plant tissues and can be easily adapted for the preparation of alternative sample formats (e.g., three-dimensional cell cultures). The integrated workflow is universally compatible with histological tissue analysis, mass spectrometry imaging and imaging mass cytometry, as well as spatial proteomic, genomic and transcriptomic tissue analysis. The protocol can be completed within 4 h and requires minimal prior experience in the preparation of tissue samples for multimodal imaging experiments.
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Affiliation(s)
- Andreas Dannhorn
- Imaging and Data analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Department of Digestion, Metabolism and Reproduction, Sir Alexander Fleming Building, Imperial College London, London, UK
| | - Emine Kazanc
- Department of Digestion, Metabolism and Reproduction, Sir Alexander Fleming Building, Imperial College London, London, UK
| | - Lucy Flint
- Imaging and Data analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Fei Guo
- Imaging and Data analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Safety Innovations, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Alfie Carter
- Imaging and Data analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
- Safety Innovations, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Andrew R Hall
- Safety Innovations, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | - Stewart A Jones
- Imaging and Data analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK
| | | | - Simon T Barry
- Bioscience, Discovery, Oncology R&D, AstraZeneca, Cambridge, UK
| | | | - Josephine Bunch
- National Centre of Excellence in Mass Spectrometry Imaging (NiCE-MSI), National Physical Laboratory, Teddington, UK
| | - Zoltan Takats
- Department of Digestion, Metabolism and Reproduction, Sir Alexander Fleming Building, Imperial College London, London, UK
| | - Richard J A Goodwin
- Imaging and Data analytics, Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
- Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK.
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Guan C, Kong L. Mass spectrometry imaging in pulmonary disorders. Clin Chim Acta 2024; 561:119835. [PMID: 38936534 DOI: 10.1016/j.cca.2024.119835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 06/24/2024] [Accepted: 06/24/2024] [Indexed: 06/29/2024]
Abstract
Mass Spectrometry Imaging (MSI) represents a novel and advancing technology that offers unparalleled in situ characterization of tissues. It provides comprehensive insights into the chemical structures, relative abundances, and spatial distributions of a vast array of both identified and unidentified endogenous and exogenous compounds, a capability not paralleled by existing analytical methodologies. Recent scholarly endeavors have increasingly explored the utility of MSI in the adjunct diagnosis and biomarker research of pulmonary disorders, including but not limited to lung cancer. Concurrently, MSI has proven instrumental in elucidating the spatiotemporal dynamics of various pharmacological agents. This review concisely delineates the fundamental principles underpinning MSI, its applications in pulmonary disease diagnosis, biomarker discovery, and drug distribution investigations. Additionally, it presents a forward-looking perspective on the prospective trajectories of MSI technological advancements.
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Affiliation(s)
- Chunliu Guan
- Key Laboratory of Environment Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China
| | - Lu Kong
- Key Laboratory of Environment Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu 210009, China.
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Zhao H, Shi C, Han W, Luo G, Huang Y, Fu Y, Lu W, Hu Q, Shang Z, Yang X. Advanced progress of spatial metabolomics in head and neck cancer research. Neoplasia 2024; 47:100958. [PMID: 38142528 PMCID: PMC10788507 DOI: 10.1016/j.neo.2023.100958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/15/2023] [Indexed: 12/26/2023]
Abstract
Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.
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Affiliation(s)
- Huiting Zhao
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China; School of Stomatology, Jinzhou Medical University, Jinzhou 121001, China
| | - Chaowen Shi
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Wei Han
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Guanfa Luo
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China
| | - Yumeng Huang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China
| | - Yujuan Fu
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China; School of Stomatology, Jinzhou Medical University, Jinzhou 121001, China
| | - Wen Lu
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China
| | - Qingang Hu
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | | | - Xihu Yang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China; School of Stomatology, Jinzhou Medical University, Jinzhou 121001, China.
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